A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults

Shengwei Wang, Qiang Zhou, Fu Xiao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

85 Citations (Scopus)

Abstract

This paper presents a strategy for fault detection and diagnosis (FDD) of HVAC systems involving sensor faults at the system level. Two schemes are involved in the system-level FDD strategy, i.e. system FDD scheme and sensor fault detection, diagnosis and estimation (FDD&E) scheme. In the system FDD scheme, one or more performance indices (PIs) are introduced to indicate the performance status (normal or faulty) of each system. Regression models are used as the benchmarks to validate the PIs computed from the actual measurements. The reliability of the system FDD is affected by the health of sensor measurements. A method based on principal component analysis (PCA) is used to detect and diagnose the sensor bias and to correct the sensor bias prior to the use of the system FDD scheme. Two interaction analyses are conducted. One is the impact of system faults on sensor FDD&E. The other is the impact of corrected sensor faults on the system FDD. It is found that the sensor FDD&E method can work well in identifying biased sensors and recovering biases even if system faults coexist, and the system FDD method is effective in diagnosing the system-level faults using processed measurements by the sensor FDD&E. Crown
Original languageEnglish
Pages (from-to)477-490
Number of pages14
JournalEnergy and Buildings
Volume42
Issue number4
DOIs
Publication statusPublished - 1 Apr 2010

Keywords

  • Fault detection
  • Fault diagnosis
  • HVAC system
  • Model-based
  • Sensor fault

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Cite this